In the context of model deployment using Spark ML, explain the process of saving and loading a trained machine learning model for inference. Provide a code snippet demonstrating the use of Spark ML's `MLWriter` for model persistence and explain the key considerations to keep in mind during this process.

Simulated

Use the save method of the trained model to save the model in a specified directory in a distributed file system like HDFS or S3.

56.5%

Use the load method of the MLReader class from the pyspark.ml module to load the saved model from the specified directory for inference.

26.1%

Use the fit method of the Spark ML estimator to train the model and then use the transform method to make predictions on new data.

10.9%

Use the predict method of the trained model to make predictions on new data without saving and loading the model.

6.5%

Databricks Certified Machine Learning - Associate